{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一.算法原理\n",
    "k均值聚类大家都很熟悉了，但可能更多是对算法的迭代过程比较熟悉，这里需要说明一下的就是k均值聚类是有“目标”的，假设给定样本$D=\\{x_1,x_2,...,x_m\\}$，针对聚类所得簇划分$C=\\{C_1,C_2,...,C_k\\}$最小化如下的平方误差函数：   \n",
    "\n",
    "$$\n",
    "C^*=arg\\min_{C}\\sum_{i=1}^k\\sum_{x\\in C_i}\\left|\\left|x-\\mu_i\\right|\\right|_2^2\n",
    "$$  \n",
    "\n",
    "其中，$\\mu_i=\\frac{1}{|C_i|}\\sum_{x\\in C_i}x$是簇$C_i$的均值向量，将$m$个样本分到$k$个簇共有$\\frac{1}{m!}\\sum_{i=1}^m(-1)^iC_m^i(m-i)^k$种可能，显然这是一个NP-hard问题，我们所熟知的k均值算法其实是对这个问题的贪心搜索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二.算法流程\n",
    ">输入：样本集$D=\\{x_1,x_2,...,x_m\\}$；聚类次数$k$；终止误差$tol$  \n",
    "\n",
    ">过程   \n",
    "\n",
    ">（1）从$D$中随机选择$k$个样本作为初始均值向量${\\mu_1,\\mu_2,...,\\mu_k}$   \n",
    "\n",
    ">（2）重复如下过程，直到终止条件   \n",
    "\n",
    ">>（2.1）令$C_i=\\{\\},i=1,2,...,k$；  \n",
    "\n",
    ">>（2.2）对$j=1,2,...,m$；   \n",
    "\n",
    ">>>（2.2.1）计算样本$x_j$与各均值向量$\\mu_i,i=1,2,...,k$：$d_{ji}=||x_j-\\mu_i||_2$；  \n",
    "\n",
    ">>>（2.2.2）根据距离最近的均值向量确定$x_j$的簇标记：$\\lambda_j=arg\\min_{i\\in \\{1,2,...,k\\}}d_{ji}$；   \n",
    "\n",
    ">>>（2.2.3）将样本$x_j$划入相应的簇$C_{\\lambda_j}=C_{\\lambda_j}\\bigcup\\{x_j\\}$；  \n",
    "\n",
    ">>（2.3）令$\\epsilon=0$，对$i=1,2,...,k$  \n",
    "\n",
    ">>>（2.3.1）计算新的均值向量：$\\mu'_i=\\frac{1}{C_i}\\sum_{x\\in C_i}x$，令$\\epsilon=\\epsilon+||u_i-u'_i||_2$\n",
    "\n",
    ">>>（2.3.2）并更新$u_i=u'_i$  \n",
    "\n",
    ">> （2.4）如果$\\epsilon<tol$，则终止循环  \n",
    "\n",
    ">输出：$C=\\{C_1,C_2,...,C_k\\}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "kmeans聚类实现，封装到ml_models.cluster\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class KMeans(object):\n",
    "    def __init__(self, k=3, epochs=100, tol=1e-3, dist_method=None):\n",
    "        \"\"\"\n",
    "        :param k: 聚类簇数量\n",
    "        :param epochs: 最大迭代次数\n",
    "        :param tol: 终止条件\n",
    "        :param dist_method:距离函数，默认欧氏距离\n",
    "        \"\"\"\n",
    "        self.k = k\n",
    "        self.epochs = epochs\n",
    "        self.tol = tol\n",
    "        self.dist_method = dist_method\n",
    "        if self.dist_method is None:\n",
    "            self.dist_method = lambda x, y: np.sqrt(np.sum(np.power(x - y, 2)))\n",
    "        self.cluster_centers_ = {}  # 记录簇中心坐标\n",
    "\n",
    "    def fit(self, X):\n",
    "        m = X.shape[0]\n",
    "        # 初始化\n",
    "        for idx, data_idx in enumerate(np.random.choice(list(range(m)), self.k, replace=False).tolist()):\n",
    "            self.cluster_centers_[idx] = X[data_idx]\n",
    "        # 迭代\n",
    "        for _ in range(self.epochs):\n",
    "            C = {}\n",
    "            for idx in range(self.k):\n",
    "                C[idx] = []\n",
    "            for j in range(m):\n",
    "                best_k = None\n",
    "                min_dist = np.infty\n",
    "                for idx in range(self.k):\n",
    "                    dist = self.dist_method(self.cluster_centers_[idx], X[j])\n",
    "                    if dist < min_dist:\n",
    "                        min_dist = dist\n",
    "                        best_k = idx\n",
    "                C[best_k].append(j)\n",
    "            # 更新\n",
    "            eps = 0\n",
    "            for idx in range(self.k):\n",
    "                vec_k = np.mean(X[C[idx]], axis=0)\n",
    "                eps += self.dist_method(vec_k, self.cluster_centers_[idx])\n",
    "                self.cluster_centers_[idx] = vec_k\n",
    "            # 判断终止条件\n",
    "            if eps < self.tol:\n",
    "                break\n",
    "\n",
    "    def predict(self, X):\n",
    "        m = X.shape[0]\n",
    "        rst = []\n",
    "        for i in range(m):\n",
    "            vec = X[i]\n",
    "            best_k = None\n",
    "            min_dist = np.infty\n",
    "            for idx in range(self.k):\n",
    "                dist = self.dist_method(self.cluster_centers_[idx], vec)\n",
    "                if dist < min_dist:\n",
    "                    min_dist = dist\n",
    "                    best_k = idx\n",
    "            rst.append(best_k)\n",
    "        return np.asarray(rst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四.测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('../')\n",
    "from ml_models import utils\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "X, y = make_blobs(n_samples=400, centers=4, cluster_std=0.85, random_state=0)\n",
    "X = X[:, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练\n",
    "kmeans = KMeans(k=4)\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23d768176a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.plot_decision_function(X, y, kmeans)\n",
    "utils.plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
